Over the past few years, Graph Convolutional Networks (GCNs) have achieved state-of-the-art performance in machine learning tasks on graph data and have been widely applied to many real-world applications across different fields, such as traffic prediction, user behavior analysis, and fraud detection. However, networks in the real world are often with heterogeneous degree distributions, such […]
Read More